This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# function to find sentences containing PMs of India | |
def find_names(text): | |
names = [] | |
# spacy doc | |
doc = nlp(text) | |
# pattern | |
pattern = [{'LOWER':'prime'}, | |
{'LOWER':'minister'}, | |
{'POS':'ADP','OP':'?'}, | |
{'POS':'PROPN'}] | |
# Matcher class object | |
matcher = Matcher(nlp.vocab) | |
matcher.add("names", None, pattern) | |
matches = matcher(doc) | |
# finding patterns in the text | |
for i in range(0,len(matches)): | |
# match: id, start, end | |
token = doc[matches[i][1]:matches[i][2]] | |
# append token to list | |
names.append(str(token)) | |
# Only keep sentences containing Indian PMs | |
for name in names: | |
if (name.split()[2] == 'of') and (name.split()[3] != "India"): | |
names.remove(name) | |
return names | |
# apply function | |
df2['PM_Names'] = df2['Sent'].apply(find_names) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Hi, it should have been df2['Sent'] in the last line. I have made the changes, it should work now.